Functional Profiling of a Plasmodium Genome Reveals an Abundance of Essential Genes.

The genomes of malaria parasites contain many genes of unknown function. To assist drug development through the identification of essential genes and pathways, we have measured competitive growth rates in mice of 2,578 barcoded Plasmodium berghei knockout mutants, representing >50% of the genome, and created a phenotype database. At a single stage of its complex life cycle, P. berghei requires two-thirds of genes for optimal growth, the highest proportion reported from any organism and a probable consequence of functional optimization necessitated by genomic reductions during the evolution of parasitism. In contrast, extreme functional redundancy has evolved among expanded gene families operating at the parasite-host interface. The level of genetic redundancy in a single-celled organism may thus reflect the degree of environmental variation it experiences. In the case of Plasmodium parasites, this helps rationalize both the relative successes of drugs and the greater difficulty of making an effective vaccine.

Using genetic association data to guide drug discovery and development: Review of methods and applications.

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Patterns of mosaicism for sequence and copy-number variants discovered through clinical deep sequencing of disease-related genes in one million individuals.

DNA variants that arise after conception can show mosaicism, varying in presence and extent among tissues. Mosaic variants have been reported in Mendelian diseases, but further investigation is necessary to broadly understand their incidence, transmission, and clinical impact. A mosaic pathogenic variant in a disease-related gene may cause an atypical phenotype in terms of severity, clinical features, or timing of disease onset. Using high-depth sequencing, we studied results from one million unrelated individuals referred for genetic testing for almost 1,900 disease-related genes. We observed 5,939 mosaic sequence or intragenic copy number variants distributed across 509 genes in nearly 5,700 individuals, constituting approximately 2% of molecular diagnoses in the cohort. Cancer-related genes had the most mosaic variants and showed age-specific enrichment, in part reflecting clonal hematopoiesis in older individuals. We also observed many mosaic variants in genes related to early-onset conditions. Additional mosaic variants were observed in genes analyzed for reproductive carrier screening or associated with dominant disorders with low penetrance, posing challenges for interpreting their clinical significance. When we controlled for the potential involvement of clonal hematopoiesis, most mosaic variants were enriched in younger individuals and were present at higher levels than in older individuals. Furthermore, individuals with mosaicism showed later disease onset or milder phenotypes than individuals with non-mosaic variants in the same genes. Collectively, the large compendium of variants, disease correlations, and age-specific results identified in this study expand our understanding of the implications of mosaic DNA variation for diagnosis and genetic counseling.

Graphormer supervised de novo protein design method and function validation.

Protein design is central to nearly all protein engineering problems, as it can enable the creation of proteins with new biological functions, such as improving the catalytic efficiency of enzymes. One key facet of protein design, fixed-backbone protein sequence design, seeks to design new sequences that will conform to a prescribed protein backbone structure. Nonetheless, existing sequence design methods present limitations, such as low sequence diversity and shortcomings in experimental validation of the designed functional proteins. These inadequacies obstruct the goal of functional protein design. To improve these limitations, we initially developed the Graphormer-based Protein Design (GPD) model. This model utilizes the Transformer on a graph-based representation of three-dimensional protein structures and incorporates Gaussian noise and a sequence random masks to node features, thereby enhancing sequence recovery and diversity. The performance of the GPD model was significantly better than that of the state-of-the-art ProteinMPNN model on multiple independent tests, especially for sequence diversity. We employed GPD to design CalB hydrolase and generated nine artificially designed CalB proteins. The results show a 1.7-fold increase in catalytic activity compared to that of the wild-type CalB and strong substrate selectivity on p-nitrophenyl acetate with different carbon chain lengths (C2-C16). Thus, the GPD method could be used for the de novo design of industrial enzymes and protein drugs. The code was released at https://github.com/decodermu/GPD.

Oxidative status: A general but overlooked indicator of welfare across animal species?

Because of their ubiquity, plasticity, and direct effects on the nervous system, markers of oxidative status may be of great value to assess animal welfare across species and conditions in the wild. However, welfare biologists have not yet seized this opportunity, possibly because the validity of these markers as welfare indicators remains questionable. A validation process was, therefore, performed here using a meta-analytical approach considering three conditions assumed to impair the welfare of animals. With very few exceptions, two of the four considered markers consistently varied across these negatively-valenced conditions. By highlighting the current underrepresentation of markers of oxidative status in animal welfare studies, and by concretely illustrating that some of these markers can consistently reflect negative affective states, this article aims to encourage biologists to include these physiological markers in their toolbox to better measure, monitor, and perhaps also improve the welfare of animals in their natural habitat.

A Targeted LC-MRM3 Proteomic Approach for the Diagnosis of SARS-CoV-2 Infection in Nasopharyngeal Swabs.

Since its first appearance, severe acute respiratory syndrome coronavirus 2 quickly spread around the world and the lack of adequate PCR testing capacities, especially during the early pandemic, led the scientific community to explore new approaches such as mass spectrometry (MS). We developed a proteomics workflow to target several tryptic peptides of the nucleocapsid protein. A highly selective multiple reaction monitoring-cubed (MRM3) strategy provided a sensitivity increase in comparison to conventional MRM acquisition. Our MRM3 approach was first tested on an Amsterdam public health cohort (alpha-variant, 760 participants) detecting viral nucleocapsid protein peptides from nasopharyngeal swabs samples presenting a cycle threshold value down to 35 with sensitivity and specificity of 94.2% and 100.0%, without immunopurification. A second iteration of the MS-diagnostic test, able to analyze more than 400 samples per day, was clinically validated on a Leiden-Rijswijk public health cohort (delta-variant, 2536 participants) achieving 99.9% specificity and 93.1% sensitivity for patients with cycle threshold values up to 35. In this manuscript, we also developed and brought the first proof of the concept of viral variant monitoring in a complex matrix using targeted MS.

Identification and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies.

Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD.

Validation of transcriptome signature reversion for drug repurposing in oncology.

Transcriptome signature reversion (TSR) has been extensively proposed and used to discover new indications for existing drugs (i.e. drug repositioning, drug repurposing) for various cancer types. TSR relies on the assumption that a drug that can revert gene expression changes induced by a disease back to original, i.e. healthy, levels is likely to be therapeutically active in treating the disease. Here, we aimed to validate the concept of TSR using the PRISM repurposing data set, which is-as of writing-the largest pharmacogenomic data set. The predictive utility of the TSR approach as it has currently been used appears to be much lower than previously reported and is completely nullified after the drug gene expression signatures are adjusted for the general anti-proliferative downstream effects of drug-induced decreased cell viability. Therefore, TSR mainly relies on generic anti-proliferative drug effects rather than on targeting cancer pathways specifically upregulated in tumor types.

CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting.

In the drug development process, approximately 30% of failures are attributed to drug safety issues. In particular, the first-in-human (FIH) trial of a new drug represents one of the highest safety risks, and initial dose selection is crucial for ensuring safety in clinical trials. With traditional dose estimation methods, which extrapolate data from animals to humans, catastrophic events have occurred during Phase I clinical trials due to interspecies differences in compound sensitivity and unknown molecular mechanisms. To address this issue, this study proposes a CrossFuse-extreme gradient boosting (XGBoost) method that can directly predict the maximum recommended daily dose of a compound based on existing human research data, providing a reference for FIH dose selection. This method not only integrates multiple features, including molecular representations, physicochemical properties and compound-protein interactions, but also improves feature selection based on cross-validation. The results demonstrate that the CrossFuse-XGBoost method not only improves prediction accuracy compared to that of existing local weighted methods [k-nearest neighbor (k-NN) and variable k-NN (v-NN)] but also solves the low prediction coverage issue of v-NN, achieving full coverage of the external validation set and enabling more reliable predictions. Furthermore, this study offers a high level of interpretability by identifying the importance of different features in model construction. The 241 features with the most significant impact on the maximum recommended daily dose were selected, providing references for optimizing the structure of new compounds and guiding experimental research. The datasets and source code are freely available at https://github.com/cqmu-lq/CrossFuse-XGBoost.

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.

Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.

Establishing finite element model credibility of a pedicle screw system under compression-bending: An end-to-end example of the ASME V&V 40 standard.

Computational modeling and simulation (CM&S) is a key tool in medical device design, development, and regulatory approval. For example, finite element analysis (FEA) is widely used to understand the mechanical integrity and durability of orthopaedic implants. The ASME V&V 40 standard and supporting FDA guidance provide a framework for establishing model credibility, enabling deeper reliance on CM&S throughout the total product lifecycle. Examples of how to apply the principles outlined in the ASME V&V 40 standard are important to facilitating greater adoption by the medical device community, but few published examples are available that demonstrate best practices. Therefore, this paper outlines an end-to-end (E2E) example of the ASME V&V 40 standard applied to an orthopaedic implant. The objective of this study was to illustrate how to establish the credibility of a computational model intended for use as part of regulatory evaluation. In particular, this study focused on whether a design change to a spinal pedicle screw construct (specifically, the addition of a cannulation to an existing non-cannulated pedicle screw) would compromise the rod-screw construct mechanical performance. This question of interest (?OI) was addressed by establishing model credibility requirements according to the ASME V&V 40 standard. Experimental testing to support model validation was performed using spinal rods and non-cannulated pedicle screw constructs made with medical grade titanium (Ti-6Al-4V ELI). FEA replicating the experimental tests was performed by three independent modelers and validated through comparisons of common mechanical properties such as stiffness and yield force. The validated model was then used to simulate F1717 compression-bending testing on the new cannulated pedicle screw design to answer the ?OI, without performing any additional experimental testing. This E2E example provides a realistic scenario for the application of the ASME V&V 40 standard to orthopedic medical device applications.

Mapping short association fibre connectivity up to V3 in the human brain in vivo.

Short association fibres (SAF) are the most abundant fibre pathways in the human white matter. Until recently, SAF could not be mapped comprehensively in vivo because diffusion weighted magnetic resonance imaging with sufficiently high spatial resolution needed to map these thin and short pathways was not possible. Recent developments in acquisition hardware and sequences allowed us to create a dedicated in vivo method for mapping the SAF based on sub-millimetre spatial resolution diffusion weighted tractography, which we validated in the human primary (V1) and secondary (V2) visual cortex against the expected SAF retinotopic order. Here, we extended our original study to assess the feasibility of the method to map SAF in higher cortical areas by including SAF up to V3. Our results reproduced the expected retinotopic order of SAF in the V2-V3 and V1-V3 stream, demonstrating greater robustness to the shorter V1-V2 and V2-V3 than the longer V1-V3 connections. The demonstrated ability of the method to map higher-order SAF connectivity patterns in vivo is an important step towards its application across the brain.

Methods to Discover and Validate Biofluid-Based Biomarkers in Neurodegenerative Dementias.

Neurodegenerative dementias are progressive diseases that cause neuronal network breakdown in different brain regions often because of accumulation of misfolded proteins in the brain extracellular matrix, such as amyloids or inside neurons or other cell types of the brain. Several diagnostic protein biomarkers in body fluids are being used and implemented, such as for Alzheimer's disease. However, there is still a lack of biomarkers for co-pathologies and other causes of dementia. Such biofluid-based biomarkers enable precision medicine approaches for diagnosis and treatment, allow to learn more about underlying disease processes, and facilitate the development of patient inclusion and evaluation tools in clinical trials. When designing studies to discover novel biofluid-based biomarkers, choice of technology is an important starting point. But there are so many technologies to choose among. To address this, we here review the technologies that are currently available in research settings and, in some cases, in clinical laboratory practice. This presents a form of lexicon on each technology addressing its use in research and clinics, its strengths and limitations, and a future perspective.

Validation of GCN5L1/BLOC1S1/BLOS1 antibodies using knockout cells and tissue.

GCN5L1, also known as BLOC1S1 and BLOS1, is a small intracellular protein involved in many key biological processes. Over the last decade, GCN5L1 has been implicated in the regulation of protein lysine acetylation, energy metabolism, endo-lysosomal function, and cellular immune pathways. An increasing number of published papers have used commercially-available reagents to interrogate GCN5L1 function. However, in many cases these reagents have not been rigorously validated, leading to potentially misleading results. In this report we tested several commercially-available antibodies for GCN5L1, and found that two-thirds of those available did not unambiguously detect the protein by western blot in cultured mouse cells or ex vivo liver tissue. These data suggest that previously published studies which used these unverified antibodies to measure GCN5L1 protein abundance, in the absence of other independent methods of corroboration, should be interpreted with appropriate caution.

Inhibition of RAS-driven signaling and tumorigenesis with a pan-RAS monobody targeting the Switch I/II pocket.

RAS mutants are major therapeutic targets in oncology with few efficacious direct inhibitors available. The identification of a shallow pocket near the Switch II region on RAS has led to the development of small-molecule drugs that target this site and inhibit KRAS(G12C) and KRAS(G12D). To discover other regions on RAS that may be targeted for inhibition, we have employed small synthetic binding proteins termed monobodies that have a strong propensity to bind to functional sites on a target protein. Here, we report a pan-RAS monobody, termed JAM20, that bound to all RAS isoforms with nanomolar affinity and demonstrated limited nucleotide-state specificity. Upon intracellular expression, JAM20 potently inhibited signaling mediated by all RAS isoforms and reduced oncogenic RAS-mediated tumorigenesis in vivo. NMR and mutation analysis determined that JAM20 bound to a pocket between Switch I and II, which is similarly targeted by low-affinity, small-molecule inhibitors, such as BI-2852, whose in vivo efficacy has not been demonstrated. Furthermore, JAM20 directly competed with both the RAF(RBD) and BI-2852. These results provide direct validation of targeting the Switch I/II pocket for inhibiting RAS-driven tumorigenesis. More generally, these results demonstrate the utility of tool biologics as probes for discovering and validating druggable sites on challenging targets.

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. METHODS: We acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. RESULTS: We harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. CONCLUSION: Innovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

Direct Eight-Electron N2O Electroreduction to NH3 Enabled by an Fe Double-Atom Catalyst.

N2O is a dominant atmosphere pollutant, causing ozone depletion and global warming. Currently, electrochemical reduction of N2O has gained increasing attention to remove N2O, but its product is worthless N2. Here, we propose a direct eight-electron (8e) pathway to electrochemically convert N2O into NH3. As a proof of concept, using density functional theory calculation, an Fe2 double-atom catalyst (DAC) anchored by N-doped porous graphene (Fe2@NG) was screened out to be the most active and selective catalyst for N2O electroreduction toward NH3 via the novel 8e pathway, which benefits from the unique bent N2O adsorption configuration. Guided by theoretical prediction, Fe2@NG DAC was fabricated experimentally, and it can achieve a high N2O-to-NH3 Faradaic efficiency of 77.8% with a large NH3 yield rate of 2.9 mg h-1 cm-2 at -0.6 V vs RHE in a neutral electrolyte. Our study offers a feasible strategy to synthesize NH3 from pollutant N2O with simultaneous N2O removal.

On the use of tandem mass spectra acquired from samples of evolutionarily distant organisms to validate methods for false discovery rate estimation.

Estimating the false discovery rate (FDR) of peptide identifications is a key step in proteomics data analysis, and many methods have been proposed for this purpose. Recently, an entrapment-inspired protocol to validate methods for FDR estimation appeared in articles showcasing new spectral library search tools. That validation approach involves generating incorrect spectral matches by searching spectra from evolutionarily distant organisms (entrapment queries) against the original target search space. Although this approach may appear similar to the solutions using entrapment databases, it represents a distinct conceptual framework whose correctness has not been verified yet. In this viewpoint, we first discussed the background of the entrapment-based validation protocols and then conducted a few simple computational experiments to verify the assumptions behind them. The results reveal that entrapment databases may, in some implementations, be a reasonable choice for validation, while the assumptions underpinning validation protocols based on entrapment queries are likely to be violated in practice. This article also highlights the need for well-designed frameworks for validating FDR estimation methods in proteomics.

Robustness evaluations of pathway activity inference methods on gene expression data.

BACKGROUND: With the exponential growth of high-throughput technologies, multiple pathway analysis methods have been proposed to estimate pathway activities from gene expression profiles. These pathway activity inference methods can be divided into two main categories: non-Topology-Based (non-TB) and Pathway Topology-Based (PTB) methods. Although some review and survey articles discussed the topic from different aspects, there is a lack of systematic assessment and comparisons on the robustness of these approaches. RESULTS: Thus, this study presents comprehensive robustness evaluations of seven widely used pathway activity inference methods using six cancer datasets based on two assessments. The first assessment seeks to investigate the robustness of pathway activity in pathway activity inference methods, while the second assessment aims to assess the robustness of risk-active pathways and genes predicted by these methods. The mean reproducibility power and total number of identified informative pathways and genes were evaluated. Based on the first assessment, the mean reproducibility power of pathway activity inference methods generally decreased as the number of pathway selections increased. Entropy-based Directed Random Walk (e-DRW) distinctly outperformed other methods in exhibiting the greatest reproducibility power across all cancer datasets. On the other hand, the second assessment shows that no methods provide satisfactory results across datasets. CONCLUSION: However, PTB methods generally appear to perform better in producing greater reproducibility power and identifying potential cancer markers compared to non-TB methods.

An XIC-Centric Strategy for Improved Identification and Quantification in Proteomic Data Analyses.

Reproducibility is a "proteomic dream" yet to be fully realized. A typical data analysis workflow utilizing extracted ion chromatograms (XICs) often treats the information path from identification to quantification as a one-way street. Here, we propose an XIC-centric approach in which the data flow is bidirectional: identifications are used to derive XICs whose information is in turn applied to validate the identifications. In this study, we employed liquid chromatography-mass spectrometry data from glycoprotein and human hair samples to illustrate the XIC-centric concept. At the core of this approach was XIC-based monoisotope repicking. Taking advantage of the intensity information for all detected isotopes across the whole range of an XIC peak significantly improved the accuracy and uncovered misidentifications originating from monoisotope assignment mistakes. It could also rescue non-top-ranked glycopeptide hits. Identification of glycopeptides is particularly susceptible to precursor mass errors for their low abundances, large masses, and glycans differing by 1 or 2 Da easily confused as isotopes. In addition, the XIC-centric strategy significantly reduced the problem of one XIC peak associated with multiple unique identifications, a source of quantitative irreproducibility. Taken together, the proposed approach can lead to improved identification and quantification accuracy and, ultimately, enhanced reproducibility in proteomic data analyses.